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@article{166213,
        author = {anand prakash srivastava and Dr. Sakuldeep Singh},
        title = {A movie recommendation system based on machine learning techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {606-610},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166213},
        abstract = {This study presents a movie recommendation system tailored to the cosine similarity for recommendations. From the tmdb dataset, a random movie is chosen, and ten similar movies are recommended using cosine similarity. The system’s effectiveness was evaluated using two machine learning algorithms: Naive Bayes (Gaussian, Multinomial, Bernoulli) and Support Vector Machine (SVM) with linear and radial basis function (rbf) kernels. Models were trained on datasets comprising 75%, and 80% of the available data, and their performance was assessed. Results indicated that the SVM method, particularly with the linear kernel, achieved the highest accuracy, while the Naive Bayes showed the lowest accuracy. The SVM algorithm’s consistent and superior performance highlights its suitability for this recommendation system, whereas Naive Bayes was less effective for this application.},
        keywords = {Cosine Similarity, Movie recommendation system, Naive Bayes, Support Vector Machine},
        month = {November},
        }